Pareto Optimal Strategy Research Articles

Multi-Objective Particle Swarm Optimization with Comparison Scheme and New Pareto-Optimal Search Strategy

In the presented article, a novel multi-objective PSO algorithm, RP-MOPSO has been proposed. The algorithm adopts a new comparison scheme for position upgrading. The scheme is simple but effective in improve algorithms convergence speed. A sigma-density strategy of selecting the global best particle for each particle in swarm based on a new solutions density definition is designed. Experimental results on seven functions show that proposed algorithm show better convergence performance than other classical MOP algorithms. Meanwhile the proposed algorithm is more effective in maintaining the diversity of the solutions.

Multi-objective optimization of heat exchanger based on entransy dissipation theory in an irreversible Brayton cycle system

A multi-objective optimization of main heat exchanger in a regenerative Brayton cycle system is carried out based on entransy dissipation. The best trade-off between the entransy dissipation numbers caused by heat transfer and fluid friction is achieved in the Pareto optimal solutions, the decrease of entransy dissipation related to heat transfer inevitably leads to the increase of entransy dissipation due to fluid friction, and vice versa. The entransy dissipation due to heat transfer rather than that due to fluid friction plays a decisive role in the net work output. The Pareto optimal schemes are widely superior to the random design schemes at both component and system levels. The diversity of Pareto design schemes is very convenient for users to choose the most appropriate design scheme according to the practical needs.

Pareto-optimal Nash equilibrium in capacity allocation game for self-managed networks

In this paper we introduce a capacity allocation game which models the problem of maximizing network utility from the perspective of distributed noncooperative agents. Motivated by the idea of self-managed networks, in the developed framework the decision-making entities are associated with individual transmission links, deciding on the way they split capacity among concurrent flows. An efficient decentralized algorithm is given for computing a strongly Pareto-optimal strategies, constituting a pure Nash equilibrium. Subsequently, we discuss the properties of the introduced game related to the Price of Anarchy and Price of Stability. The paper is concluded with an experimental study.

Robust Optimal Operation of Two‐Chamber Microbial Fuel Cell System Under Uncertainty: A Stochastic Simulation Based Multi‐Objective Genetic Algorithm Approach

AbstractThis investigation is performed to study the optimal operation decision of two‐chamber microbial fuel cell (MFC) system under uncertainty. To gain insight into the mechanism of uncertainty propagation, a Quasi‐Monte Carlo method‐based stochastic analysis is conducted not only to elucidate the effect of each uncertain parameter on the variability of power density output, but also to illustrate the interactive effects of the all uncertain parameters on the performance of MFC. Moreover, a systematic stochastic simulation‐based multi‐objective genetic algorithm framework is proposed to identify a set of Pareto‐optimal robust operation strategies, which is helpful to provide an imperative insight into the relationship between the mean and standard deviation of output power density. The results indicate that (1) the coefficient of variance (COV) value of output power density has a linear relationship with the COV value of each uncertainty parameter as well as all interactive parameters; and (2) a significant performance improvement with respect to both mean and standard deviation of power density is observed by implementing the multi‐objective robust optimization. These results thus validate that the proposed uncertainty analysis and robust optimization framework provide a promising tool for robust optimal design and operation of fuel cell systems under uncertainty.

Brand Franchise Supply Chain Partnership Based on Online and Offline Integrating Strategy

Taking advantage of online shopping affecting major parameters of corporate business including customer experience, customer word-of-mouth diffusion and customer complaints sharing, price elastic of consumer demand, and consumer brand loyalty, this paper focuses on the role of brand franchise supply chain partnership in the online and offline integrating environment. The findings suggest that the transfer payment shall be the critical contract clause to coordinate the brand franchise supply chain partnership to implement Pareto optimal strategy on VMI policy. Moreover, the optimal transfer payment has a strong positive relativity with the complementary of the online-offline shopping and VMI scale economic effect, whereas negative relativity with the substitutability of the online-offline shopping and VMI holding cost. The more VMI scale economic effect enhances, the larger online-offline integrating shopping market shares and the more system revenue shall be obtained in the brand franchise supply chain whereas taking advantage of the less transfer payment. The more VMI holding cost decreases, the larger online-offline integrating shopping market shares and the more system revenue shall be obtained in the brand franchise supply chain whereas taking advantage of the less transfer payment.

Evolutionary Social Learning Through Networks

We study evolutionary learning processes through social networks. We show that network heterogeneity, in particular scale-freeness, enhances social learning by studying a model in which an agent set on a network imitates the strategy only from the neighborhood. Further, in order to determine whether it is the heterogeneity of the game network (which determines who plays with whom) or the heterogeneity of the learning network (which determines who learns from whom) that enhances social learning, we study another model wherein a network of learning exists but a game does not exist. We show that network heterogeneity enhances social learning not because the network of learning is heterogeneous but because the network of games is heterogeneous. Moreover, we also study a coordination game on a network. We derive the condition that a Pareto optimal strategy spreads over time. We show that Pareto optimal strategy spreads if the players taking the Pareto optimal strategy are on hub vertices in the beginning.

An Auction-Based Pareto-Optimal Strategy for Dynamic and Fair Allotment of Resources in Wireless Mobile Networks

Ongoing advances in sophisticated mobile computing technologies and wireless communications have propelled substantial research work toward designing and implementing a new breed of mobile communications systems. One of the fundamental design objectives of such systems is to ensure complete roaming ability for the mobile users. In addition, upon handoff events, the mobile users also require to be able to perform renegotiations pertaining to their required quality-of-service (QoS) requirements with the concerned system. In this paper, we envision a fair and dynamic auction-based QoS negotiation scheme to deal with this issue. The envisioned scheme provides the mobile users with the flexibility to dynamically negotiate or renegotiate their preferred service levels with the corresponding service provider. The proposed technique has three crucial design objectives: First, it ensures a high level of fairness among the competing mobile users (each with a specific budget). Second, it ensures efficient utilization of the available network resources. Finally, its auction-based mechanism aims at maximizing the revenue of the service provider. A mathematical analysis is provided to demonstrate that, when the three design goals are taken into account, the resource allocation function that the proposed scheme provides represents a Pareto-optimal solution. The effectiveness of the proposed scheme is also verified through extensive simulations.

Two-agent Pareto optimal cooperative investment in general semimartingale model

We consider the problem of expected utility maximization for the two-agent case in general semimartingale model. For this case a cooperative investment game is posed as follows: firstly collect both agents' capital as a whole at the initial time, and invest it in a trading strategy. Then at some time T 0 one agent quits cooperation and terminates investment, so they divide the wealth and each of them gets a part. During the time interval [T 0, T], the other agent invests her capital in a new trading strategy herself. By stochastic optimization methods with the help of the theory of backward stochastic differential equations, we give a characterization of Pareto optimal cooperative strategies and a characterization of situations where cooperation strictly Pareto dominates non-cooperation in our model.

Two-agent Pareto optimal cooperative investment in incomplete market: An equivalent characterization

This paper studies the following cooperative investment game with two agents. At the start of the game, both the agents’ capital are collected. The total capital are then invested according to a certain trading strategy. At a certain time T0 one agent quits the cooperation and they divide the wealth among themselves. During the remaining period [T0, T], the other agent invests his/her capital following a possibly different trading strategy. By stochastic optimization method combined with the theory of Backward Stochastic Differential Equations (BSDEs, for short), we give an equivalent characterization of the Pareto optimal cooperative strategies.

Optimization of the angle of attack of delta-winglet vortex generators in a plate-fin-and-tube heat exchanger

Delta-winglet vortex generators (VGs) are known to enhance the heat transfer between the energy-carrying fluid and the heat transfer surfaces in plate-fin-and-tube banks. In this study optimal angles of attack of the delta-winglets are investigated based on the Pareto optimal strategy. The optimization process combines a CFD analysis, genetic algorithms and the response surface methodology. The angle of attack of a pair a delta-winglet-type VGs mounted behind each tube is varied between β = −90° and +90°. Three circular tube rows with inline and staggered tube arrangements are investigated for Reynolds numbers from 200 to 1200 (based on the inlet height and inlet velocity). The flow structure and heat transfer behavior is analyzed in detail for certain cases and the staggered and the inline tube arrangements are compared. Finally, for each of these arrangements the optimal sets of angles of attack for different Reynolds numbers are presented.

Experimental implementation of a four-player quantum game

Game theory is central to the understanding of competitive interactions arising in many fields, from the social and physical sciences to economics. Recently, as the definition of information is generalized to include entangled quantum systems, quantum game theory has emerged as a framework for understanding the competitive flow of quantum information. Up till now, only two- and three-player quantum games have been demonstrated with restricted strategy sets. Here, we report the first experiment that implements a four-player quantum minority game over tunable four-partite entangled states encoded in the polarization of single photons. Experimental application of appropriate player strategies gives equilibrium payoff values well above those achievable in the classical game. These results are in excellent quantitative agreement with our theoretical analysis of the symmetric Pareto optimal strategies. Our results demonstrate for the first time how nontrivial equilibria can arise in a competitive situation involving quantum agents.

Computational aspects of optimal schedules with power control and multiple bitrates selection in multihop wireless networks

A significant amount of research on optimal scheduling has focused on the case where links can transmit with only a single bit-rate and a single transmission power. This paper develops techniques to accommodate multiple bit-rates and multiple transmission powers as well as a continuum of transmission powers. Optimizing over a large number of bit-rates and transmission powers typically increases the computed throughput at the expense of increasing the computation time. This paper examines the trade-off between computed throughput and computation time, with emphasis on the case when the relationship between bit-rate and SINR is the same as it is for 802.11a/g. One finding of this paper is a progression of Pareto optimal schemes that increases the computed capacity. A second finding is that optimizing over two transmission powers and two or three bit-rates achieves nearly the same throughput as optimizing over a continuum of transmission powers and all supported bit-rates. However, optimizing over a continuum of transmission powers and all supported bit-rates is far more computationally complex than optimizing over two transmission powers and a few bit-rates.

Pareto Optimal Strategy for Stochastic Weakly Coupled Large Scale Systems With State Dependent System Noise

This note is concerned with the decentralized infinite horizon stochastic Pareto-optimal static output feedback strategy for a class of weakly coupled systems with state-dependent noise. First, Pareto-optimal control problems are formulated using a static output feedback strategy. The necessary conditions are given by the cross-coupled stochastic algebraic Riccati-type equations (CSAREs) for proving the existence of the static output feedback strategy that minimizes the quadratic cost function. After determining the asymptotic structure of the solutions of the CSAREs, a new sequential numerical algorithm and Newton's method for solving the CSAREs are described. The resulting numerical solution is used to develop the Pareto-optimal strategy. Finally, the efficiency of the proposed algorithm is demonstrated by solving a numerical example for a practical megawatt-frequency control problem.

Ranking strategies and threats: a cost-based pareto optimization approach

Skyline queries have gained attention as an effective way to identify desirable objects that are "not dominated" by another object in the dataset. From market perspective, such objects are favored as pareto-optimal choices, as each of such objects has at least one competitive edge against all other objects, or not dominated. In other words, non-skyline objects have room for pareto-optimal improvements for more favorable positioning in the market. The goal of this paper is, for such non-skyline objects, to identify the cost-minimal pareto-optimal improvement strategy. More specifically, we abstract this problem as a mixed integer programming problem and develop a novel algorithm for efficiently identifying the optimal solution. In addition, the problem can be reversed to identify, for a skyline product, top-k threats that can be competitors after pareto-optimal improvements with the k lowest costs. Through extensive experiments using synthetic and real-life datasets, we show that our proposed framework is both efficient and scalable.

Cooperative resource allocation games in shared networks: symmetric and asymmetric fair bargaining models

The high cost associated with the rollout of 3G services encourages operators to share network infrastructure. Network sharing poses a new challenge in devising fair and Pareto optimal resource allocation strategies to distribute system resources among users and operators in the network. Cooperative game theory provides a framework for formulating such strategies. In this paper, we propose two models (i.e. symmetric and asymmetric) for cooperative resource bargaining among the users and mobile virtual network operators (MVNOs) of each operator in shared networks based on the concept of preference functions. The bargaining solutions proposed vary according to a parameter beta that considers the tradeoff between one's gain and the losses of others. The well-known Nash and Raiffa- Kalai-Smorodinsky solutions are special instances of the solutions proposed. The symmetric model assumes that all players have equal bargaining powers while in the asymmetric case, players are allowed to submit bids to the network operator to influence the final bargaining outcome. Due to the diversity of demand patterns, temporary resource exchange among operators can provide benefits in terms of better communication quality to their users. To avoid selfish behaviour of the operators, we propose a resource sharing model that allocates extra resources based on the past allocations and contributions of each operator.

平水中全抵抗と波浪中抵抗増加の低減を目指した船型最適化の研究

Practical ships need to have low total resistance in waves. The present paper proposes a hull optimization method for reducing resistance in still water and added resistance in wave, using the real-coded genetic algorithms. The real-coded genetic algorithm of the optimization method, employs the crossover operator for combining of the methods called the LUNDX-m(UNDX on m Latent Variables) and the EDX(Extrapolation-Directed Crossover), and employs the modified POSS (Pareto Optimal Selection Strategy) as a generation-alternation model. The panel-shift type Rankine source method is used for the calculation of wave-making resistance. The estimate formula based on statistical analysis is used for the form factor. Takahashi's formula and Maruo's formula are used for added resistance in waves. The hull optimization for KRISO 3000TEU container ship is carried out using the program which is based on the present method. In the optimization, many Pareto solutions are obtained. It is confirmed that these hull forms have low total resistance in still water and added resistance in waves comparing with the original form.

造波抵抗と砕波の低減を目的とした船型改良法に関する研究

Ships with underwater acoustic equipments are required high propulsive performance and low noise. This requirement means, from view point of wave-making characteristics, these kinds of ships need hull form with low wave-making resistance and small wave-breaking. In general, the operation speed between cruising and observing are different. In other words, it is necessary to improve different wave-making characteristics at two different speeds. The present paper proposes a hull optimization method for reducing wave-making resistance and wave-breaking, using the real-coded genetic algorithms. The real-coded genetic algorithm of the optimization method, employs a crossover operator for combining of the methods called LUNDX-m(UNDX on m Latent Variables) and EDX(Extrapolation-Directed Crossover), and employs POSS (Pareto Optimal Selection Strategy) as a generation-alternation model. Panel-shift type Rankine source method is used for the calculation of wave-making resistance as the first objective function. The gradient of calculated wave-height is used as the second objective function for an indicator of wave-breaking. The hull optimization for Model 5415 is carried out using the program which is based on the present method. In the optimization, many Pareto solutions are obtained. It is confirmed that these hull forms have low wave-making resistance and small wave- breaking comparing with the original form, by the Rankine source method.

Improving the quality of business and quality of experience in web services through prioritising and scheduling

The growth and proliferation of web services has necessitated new perspectives on metrics for such dynamic and open models of interactions between providers and consumers of services. In addition to traditional Quality of Service (QoS) metrics, there is focus on Quality of Experience (QoE) (which pertains to the user's expectations and perception of service levels) and Quality of Business (QoBiz) (which pertains to considerations such as the service provider's revenue/profit). In this paper, we present a pareto-optimal strategy that aims to satisfy users expected service levels while at the same time improving revenue and throughput of the service providers. Our three-stage model Filter-Predictor-Scheduler (FPS) prioritises and schedules service requests such that both QoE and QoBiz levels are maintained. We present extensive experimental validation of our scheduling algorithm to demonstrate its effectiveness.

NUMERICAL COMPUTATION OF PARETO OPTIMAL STRATEGY FOR GENERAL MULTIPARAMETER SINGULARLY PERTURBED SYSTEMS

In this paper, Pareto optimal strategy for general multiparameter singularly perturbed systems is investigated. The main contribution is to propose a new computational method for obtaining the high-order Pareto near-optimal strategy. Newton's method and two fixed point algorithms are combined. As a result, the new iterative algorithm achieves the quadratic convergence property and succeeds in reducing the computing workspace dramatically. It is newly shown that the resulting optimal strategy achieves the cost functional Jj* + O(||μ||2i).

Multi-agent investment in incomplete markets

The problem of the expected utility maximization in incomplete markets for a single agent is well understood in a fairly general setting. This paper studies the problem for the multi-agent case. For this case a cooperative investment game is posed as follows: firstly collect all agents’ capital together at the initial time, then invest the total capital in a trading strategy, and finally divide the terminal wealth of the trading strategy and each of them gets a part. We give a characterization of Pareto optimal cooperative strategies and a characterization of situations where cooperation strictly Pareto dominates non cooperation, and prove that the core of the cooperative investment game is non-empty under mild conditions using Scarf theorem.